Abstract:
The Web has rapidly changed the way of gathering opinions about certain assets from word-of-mouth to browsing online feedbacks. In doing so, one has to go through a huge amount of opiniated data available on internet and analyze it to get the user reviews about a particular product or service. It becomes rather tedious for a human to manually go through each and every review in order to evaluate the product, as the volume of such data available online is huge and scattered across different sources such as websites, blogs, twitter, etc. The need for an automated system to analyze such data and generate reliable results is one of the root causes of upsurge in research on sentiment analysis and opinion mining. In this paper we propose a system which evaluates any product or service based on sentiment analysis of the online reviews. We use a dual sentiment analysis method to classify reviews into three classes- positive, negative and neutral. Classifier used is Na´ve Bayes classifier.